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96 lines
2.6 KiB
Python
96 lines
2.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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import pytest
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import requests
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from tests.utils import RemoteOpenAIServer
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from vllm.entrypoints.pooling.classify.protocol import ClassificationResponse
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VLM_MODEL_NAME = "muziyongshixin/Qwen2.5-VL-7B-for-VideoCls"
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MAXIMUM_VIDEOS = 1
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TEST_VIDEO_URL = "https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4"
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HF_OVERRIDES = {
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"text_config": {
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"architectures": ["Qwen2_5_VLForSequenceClassification"],
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},
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}
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@pytest.fixture(scope="module")
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def server_vlm_classify():
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args = [
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"--runner",
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"pooling",
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"--max-model-len",
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"5000",
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"--enforce-eager",
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"--limit-mm-per-prompt",
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json.dumps({"video": MAXIMUM_VIDEOS}),
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]
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with RemoteOpenAIServer(
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VLM_MODEL_NAME, args, override_hf_configs=HF_OVERRIDES
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) as remote_server:
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yield remote_server
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@pytest.mark.parametrize("model_name", [VLM_MODEL_NAME])
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def test_classify_accepts_chat_text_only(
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server_vlm_classify: RemoteOpenAIServer, model_name: str
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) -> None:
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Please classify this text request."},
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],
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}
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]
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response = requests.post(
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server_vlm_classify.url_for("classify"),
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json={"model": model_name, "messages": messages},
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)
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response.raise_for_status()
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output = ClassificationResponse.model_validate(response.json())
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assert output.object == "list"
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assert output.model == model_name
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assert len(output.data) == 1
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assert len(output.data[0].probs) == 2
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assert output.usage.prompt_tokens == 22
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@pytest.mark.parametrize("model_name", [VLM_MODEL_NAME])
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def test_classify_accepts_chat_video_url(
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server_vlm_classify: RemoteOpenAIServer, model_name: str
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) -> None:
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Please classify this video."},
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{"type": "video_url", "video_url": {"url": TEST_VIDEO_URL}},
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],
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}
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]
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response = requests.post(
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server_vlm_classify.url_for("classify"),
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json={"model": model_name, "messages": messages},
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)
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response.raise_for_status()
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output = ClassificationResponse.model_validate(response.json())
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assert output.object == "list"
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assert output.model == model_name
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assert len(output.data) == 1
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assert len(output.data[0].probs) == 2
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assert output.usage.prompt_tokens == 4807
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